{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "signal_data=np.cos(np.arange(1600)*(20*np.pi/1000))[:,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_x=np.arange(1600)\n",
    "plot_y=signal_data\n",
    "plt.plot(plot_x,plot_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(signal_data,look_back=1):\n",
    "    dataX,dataY=[],[]\n",
    "    for i in range(len(signal_data)-look_back):\n",
    "        dataX.append(signal_data[i:(i+look_back),0])\n",
    "        dataY.append(signal_data[i+look_back,0])\n",
    "    return np.array(dataX),np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM,Dropout\n",
    "\n",
    "look_back=40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "signal_data=scaler.fit_transform(signal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=signal_data[0:800]\n",
    "val=signal_data[800:1200]\n",
    "test=signal_data[1200:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,y_train=create_dataset(train,look_back)\n",
    "x_val,y_val=create_dataset(val,look_back)\n",
    "x_test,y_test=create_dataset(test,look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
    "x_val=np.reshape(x_val,(x_val.shape[0],x_val.shape[1],1))\n",
    "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=np.squeeze(x_train)\n",
    "x_val=np.squeeze(x_val)\n",
    "x_test=np.squeeze(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(32,input_dim=40,activation='relu'))\n",
    "model.add(Dropout(0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2):\n",
    "    model.add(Dense(32,activation='relu'))\n",
    "    model.add(Dropout(0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adagrad')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 760 samples, validate on 360 samples\n",
      "Epoch 1/200\n",
      "760/760 [==============================] - 0s 556us/step - loss: 0.0993 - val_loss: 0.0656\n",
      "Epoch 2/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0500 - val_loss: 0.0377\n",
      "Epoch 3/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0390 - val_loss: 0.0538\n",
      "Epoch 4/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0325 - val_loss: 0.0450\n",
      "Epoch 5/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0333 - val_loss: 0.0334\n",
      "Epoch 6/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0282 - val_loss: 0.0293\n",
      "Epoch 7/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0293 - val_loss: 0.0371\n",
      "Epoch 8/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0271 - val_loss: 0.0317\n",
      "Epoch 9/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0276 - val_loss: 0.0363\n",
      "Epoch 10/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0239 - val_loss: 0.0387\n",
      "Epoch 11/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0220 - val_loss: 0.0380\n",
      "Epoch 12/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0199 - val_loss: 0.0342\n",
      "Epoch 13/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0214 - val_loss: 0.0248\n",
      "Epoch 14/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0202 - val_loss: 0.0354\n",
      "Epoch 15/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0189 - val_loss: 0.0294\n",
      "Epoch 16/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0192 - val_loss: 0.0350\n",
      "Epoch 17/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0182 - val_loss: 0.0336\n",
      "Epoch 18/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0183 - val_loss: 0.0259\n",
      "Epoch 19/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0177 - val_loss: 0.0290\n",
      "Epoch 20/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0169 - val_loss: 0.0291\n",
      "Epoch 21/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0167 - val_loss: 0.0311\n",
      "Epoch 22/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0156 - val_loss: 0.0309\n",
      "Epoch 23/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0164 - val_loss: 0.0336\n",
      "Epoch 24/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0180 - val_loss: 0.0261\n",
      "Epoch 25/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0161 - val_loss: 0.0294\n",
      "Epoch 26/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0177 - val_loss: 0.0237\n",
      "Epoch 27/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0171 - val_loss: 0.0267\n",
      "Epoch 28/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0169 - val_loss: 0.0255\n",
      "Epoch 29/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0153 - val_loss: 0.0319\n",
      "Epoch 30/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0150 - val_loss: 0.0286\n",
      "Epoch 31/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0164 - val_loss: 0.0266\n",
      "Epoch 32/200\n",
      "760/760 [==============================] - 0s 121us/step - loss: 0.0161 - val_loss: 0.0277\n",
      "Epoch 33/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0152 - val_loss: 0.0281\n",
      "Epoch 34/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0161 - val_loss: 0.0292\n",
      "Epoch 35/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0146 - val_loss: 0.0313\n",
      "Epoch 36/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0153 - val_loss: 0.0269\n",
      "Epoch 37/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0160 - val_loss: 0.0288\n",
      "Epoch 38/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0158 - val_loss: 0.0271\n",
      "Epoch 39/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0147 - val_loss: 0.0246\n",
      "Epoch 40/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0137 - val_loss: 0.0270\n",
      "Epoch 41/200\n",
      "760/760 [==============================] - 0s 124us/step - loss: 0.0144 - val_loss: 0.0273\n",
      "Epoch 42/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0136 - val_loss: 0.0246\n",
      "Epoch 43/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0142 - val_loss: 0.0281\n",
      "Epoch 44/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0139 - val_loss: 0.0262\n",
      "Epoch 45/200\n",
      "760/760 [==============================] - 0s 117us/step - loss: 0.0128 - val_loss: 0.0251\n",
      "Epoch 46/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0151 - val_loss: 0.0272\n",
      "Epoch 47/200\n",
      "760/760 [==============================] - 0s 122us/step - loss: 0.0132 - val_loss: 0.0269\n",
      "Epoch 48/200\n",
      "760/760 [==============================] - 0s 122us/step - loss: 0.0137 - val_loss: 0.0282\n",
      "Epoch 49/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0126 - val_loss: 0.0262\n",
      "Epoch 50/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0141 - val_loss: 0.0223\n",
      "Epoch 51/200\n",
      "760/760 [==============================] - 0s 122us/step - loss: 0.0131 - val_loss: 0.0258\n",
      "Epoch 52/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0149 - val_loss: 0.0255\n",
      "Epoch 53/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0129 - val_loss: 0.0259\n",
      "Epoch 54/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0137 - val_loss: 0.0229\n",
      "Epoch 55/200\n",
      "760/760 [==============================] - 0s 121us/step - loss: 0.0131 - val_loss: 0.0234\n",
      "Epoch 56/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0126 - val_loss: 0.0273\n",
      "Epoch 57/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0133 - val_loss: 0.0261\n",
      "Epoch 58/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0132 - val_loss: 0.0245\n",
      "Epoch 59/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0123 - val_loss: 0.0232\n",
      "Epoch 60/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0129 - val_loss: 0.0256\n",
      "Epoch 61/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0113 - val_loss: 0.0240\n",
      "Epoch 62/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0118 - val_loss: 0.0250\n",
      "Epoch 63/200\n",
      "760/760 [==============================] - 0s 122us/step - loss: 0.0136 - val_loss: 0.0268\n",
      "Epoch 64/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0110 - val_loss: 0.0242\n",
      "Epoch 65/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0126 - val_loss: 0.0259\n",
      "Epoch 66/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0112 - val_loss: 0.0251\n",
      "Epoch 67/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0120 - val_loss: 0.0244\n",
      "Epoch 68/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0111 - val_loss: 0.0230\n",
      "Epoch 69/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0124 - val_loss: 0.0237\n",
      "Epoch 70/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0126 - val_loss: 0.0256\n",
      "Epoch 71/200\n",
      "760/760 [==============================] - 0s 107us/step - loss: 0.0116 - val_loss: 0.0246\n",
      "Epoch 72/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0131 - val_loss: 0.0211\n",
      "Epoch 73/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0107 - val_loss: 0.0244\n",
      "Epoch 74/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0110 - val_loss: 0.0235\n",
      "Epoch 75/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0118 - val_loss: 0.0253\n",
      "Epoch 76/200\n",
      "760/760 [==============================] - 0s 117us/step - loss: 0.0127 - val_loss: 0.0226\n",
      "Epoch 77/200\n",
      "760/760 [==============================] - 0s 140us/step - loss: 0.0125 - val_loss: 0.0238\n",
      "Epoch 78/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0119 - val_loss: 0.0224\n",
      "Epoch 79/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0111 - val_loss: 0.0232\n",
      "Epoch 80/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0110 - val_loss: 0.0231\n",
      "Epoch 81/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0116 - val_loss: 0.0240\n",
      "Epoch 82/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0122 - val_loss: 0.0233\n",
      "Epoch 83/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0098 - val_loss: 0.0229\n",
      "Epoch 84/200\n",
      "760/760 [==============================] - 0s 117us/step - loss: 0.0119 - val_loss: 0.0222\n",
      "Epoch 85/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0107 - val_loss: 0.0232\n",
      "Epoch 86/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0108 - val_loss: 0.0218\n",
      "Epoch 87/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0118 - val_loss: 0.0210\n",
      "Epoch 88/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0113 - val_loss: 0.0230\n",
      "Epoch 89/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0114 - val_loss: 0.0226\n",
      "Epoch 90/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0100 - val_loss: 0.0221\n",
      "Epoch 91/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0111 - val_loss: 0.0222\n",
      "Epoch 92/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0107 - val_loss: 0.0232\n",
      "Epoch 93/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0103 - val_loss: 0.0217\n",
      "Epoch 94/200\n",
      "760/760 [==============================] - 0s 98us/step - loss: 0.0115 - val_loss: 0.0213\n",
      "Epoch 95/200\n",
      "760/760 [==============================] - 0s 120us/step - loss: 0.0097 - val_loss: 0.0219\n",
      "Epoch 96/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0104 - val_loss: 0.0220\n",
      "Epoch 97/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0109 - val_loss: 0.0221\n",
      "Epoch 98/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0109 - val_loss: 0.0225\n",
      "Epoch 99/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0120 - val_loss: 0.0197\n",
      "Epoch 100/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0112 - val_loss: 0.0226\n",
      "Epoch 101/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0104 - val_loss: 0.0216\n",
      "Epoch 102/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0112 - val_loss: 0.0197\n",
      "Epoch 103/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0110 - val_loss: 0.0203\n",
      "Epoch 104/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0091 - val_loss: 0.0203\n",
      "Epoch 105/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0119 - val_loss: 0.0202\n",
      "Epoch 106/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0092 - val_loss: 0.0210\n",
      "Epoch 107/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0102 - val_loss: 0.0198\n",
      "Epoch 108/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0097 - val_loss: 0.0201\n",
      "Epoch 109/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0113 - val_loss: 0.0217\n",
      "Epoch 110/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0103 - val_loss: 0.0203\n",
      "Epoch 111/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0113 - val_loss: 0.0205\n",
      "Epoch 112/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0111 - val_loss: 0.0199\n",
      "Epoch 113/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0108 - val_loss: 0.0197\n",
      "Epoch 114/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0102 - val_loss: 0.0204\n",
      "Epoch 115/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0097 - val_loss: 0.0198\n",
      "Epoch 116/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0097 - val_loss: 0.0179\n",
      "Epoch 117/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0093 - val_loss: 0.0192\n",
      "Epoch 118/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0100 - val_loss: 0.0215\n",
      "Epoch 119/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0106 - val_loss: 0.0198\n",
      "Epoch 120/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0096 - val_loss: 0.0193\n",
      "Epoch 121/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0101 - val_loss: 0.0208\n",
      "Epoch 122/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0097 - val_loss: 0.0188\n",
      "Epoch 123/200\n",
      "760/760 [==============================] - 0s 123us/step - loss: 0.0110 - val_loss: 0.0196\n",
      "Epoch 124/200\n",
      "760/760 [==============================] - 0s 129us/step - loss: 0.0104 - val_loss: 0.0197\n",
      "Epoch 125/200\n",
      "760/760 [==============================] - 0s 133us/step - loss: 0.0094 - val_loss: 0.0198\n",
      "Epoch 126/200\n",
      "760/760 [==============================] - 0s 128us/step - loss: 0.0096 - val_loss: 0.0203\n",
      "Epoch 127/200\n",
      "760/760 [==============================] - 0s 127us/step - loss: 0.0097 - val_loss: 0.0203\n",
      "Epoch 128/200\n",
      "760/760 [==============================] - 0s 134us/step - loss: 0.0106 - val_loss: 0.0228\n",
      "Epoch 129/200\n",
      "760/760 [==============================] - 0s 126us/step - loss: 0.0100 - val_loss: 0.0212\n",
      "Epoch 130/200\n",
      "760/760 [==============================] - 0s 128us/step - loss: 0.0092 - val_loss: 0.0194\n",
      "Epoch 131/200\n",
      "760/760 [==============================] - 0s 137us/step - loss: 0.0105 - val_loss: 0.0206\n",
      "Epoch 132/200\n",
      "760/760 [==============================] - 0s 135us/step - loss: 0.0105 - val_loss: 0.0199\n",
      "Epoch 133/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0091 - val_loss: 0.0202\n",
      "Epoch 134/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0112 - val_loss: 0.0185\n",
      "Epoch 135/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0102 - val_loss: 0.0200\n",
      "Epoch 136/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0094 - val_loss: 0.0191\n",
      "Epoch 137/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0111 - val_loss: 0.0192\n",
      "Epoch 138/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0106 - val_loss: 0.0191\n",
      "Epoch 139/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0092 - val_loss: 0.0196\n",
      "Epoch 140/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0103 - val_loss: 0.0176\n",
      "Epoch 141/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0105 - val_loss: 0.0184\n",
      "Epoch 142/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0107 - val_loss: 0.0163\n",
      "Epoch 143/200\n",
      "760/760 [==============================] - 0s 116us/step - loss: 0.0099 - val_loss: 0.0199\n",
      "Epoch 144/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0095 - val_loss: 0.0183\n",
      "Epoch 145/200\n",
      "760/760 [==============================] - 0s 124us/step - loss: 0.0102 - val_loss: 0.0178\n",
      "Epoch 146/200\n",
      "760/760 [==============================] - 0s 127us/step - loss: 0.0098 - val_loss: 0.0174\n",
      "Epoch 147/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0091 - val_loss: 0.0183\n",
      "Epoch 148/200\n",
      "760/760 [==============================] - 0s 117us/step - loss: 0.0097 - val_loss: 0.0173\n",
      "Epoch 149/200\n",
      "760/760 [==============================] - 0s 115us/step - loss: 0.0104 - val_loss: 0.0192\n",
      "Epoch 150/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0097 - val_loss: 0.0184\n",
      "Epoch 151/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0098 - val_loss: 0.0200\n",
      "Epoch 152/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0100 - val_loss: 0.0180\n",
      "Epoch 153/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0106 - val_loss: 0.0179\n",
      "Epoch 154/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0099 - val_loss: 0.0168\n",
      "Epoch 155/200\n",
      "760/760 [==============================] - 0s 119us/step - loss: 0.0098 - val_loss: 0.0161\n",
      "Epoch 156/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0100 - val_loss: 0.0165\n",
      "Epoch 157/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "760/760 [==============================] - 0s 110us/step - loss: 0.0089 - val_loss: 0.0180\n",
      "Epoch 158/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0097 - val_loss: 0.0159\n",
      "Epoch 159/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0091 - val_loss: 0.0169\n",
      "Epoch 160/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0094 - val_loss: 0.0180\n",
      "Epoch 161/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0097 - val_loss: 0.0158\n",
      "Epoch 162/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0094 - val_loss: 0.0168\n",
      "Epoch 163/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0104 - val_loss: 0.0170\n",
      "Epoch 164/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0087 - val_loss: 0.0163\n",
      "Epoch 165/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0090 - val_loss: 0.0184\n",
      "Epoch 166/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0092 - val_loss: 0.0177\n",
      "Epoch 167/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0099 - val_loss: 0.0178\n",
      "Epoch 168/200\n",
      "760/760 [==============================] - 0s 107us/step - loss: 0.0088 - val_loss: 0.0171\n",
      "Epoch 169/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0097 - val_loss: 0.0177\n",
      "Epoch 170/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0085 - val_loss: 0.0182\n",
      "Epoch 171/200\n",
      "760/760 [==============================] - ETA: 0s - loss: 0.008 - 0s 110us/step - loss: 0.0092 - val_loss: 0.0169\n",
      "Epoch 172/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0081 - val_loss: 0.0178\n",
      "Epoch 173/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0095 - val_loss: 0.0174\n",
      "Epoch 174/200\n",
      "760/760 [==============================] - 0s 138us/step - loss: 0.0100 - val_loss: 0.0175\n",
      "Epoch 175/200\n",
      "760/760 [==============================] - 0s 144us/step - loss: 0.0110 - val_loss: 0.0162\n",
      "Epoch 176/200\n",
      "760/760 [==============================] - 0s 139us/step - loss: 0.0100 - val_loss: 0.0169\n",
      "Epoch 177/200\n",
      "760/760 [==============================] - 0s 144us/step - loss: 0.0101 - val_loss: 0.0162\n",
      "Epoch 178/200\n",
      "760/760 [==============================] - 0s 139us/step - loss: 0.0089 - val_loss: 0.0174\n",
      "Epoch 179/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0100 - val_loss: 0.0162\n",
      "Epoch 180/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0088 - val_loss: 0.0161\n",
      "Epoch 181/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0090 - val_loss: 0.0167\n",
      "Epoch 182/200\n",
      "760/760 [==============================] - 0s 108us/step - loss: 0.0094 - val_loss: 0.0160\n",
      "Epoch 183/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0087 - val_loss: 0.0160\n",
      "Epoch 184/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0106 - val_loss: 0.0150\n",
      "Epoch 185/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0095 - val_loss: 0.0146\n",
      "Epoch 186/200\n",
      "760/760 [==============================] - 0s 114us/step - loss: 0.0099 - val_loss: 0.0155\n",
      "Epoch 187/200\n",
      "760/760 [==============================] - 0s 107us/step - loss: 0.0094 - val_loss: 0.0166\n",
      "Epoch 188/200\n",
      "760/760 [==============================] - 0s 109us/step - loss: 0.0094 - val_loss: 0.0158\n",
      "Epoch 189/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0082 - val_loss: 0.0161\n",
      "Epoch 190/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0085 - val_loss: 0.0165\n",
      "Epoch 191/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0092 - val_loss: 0.0174\n",
      "Epoch 192/200\n",
      "760/760 [==============================] - 0s 113us/step - loss: 0.0087 - val_loss: 0.0139\n",
      "Epoch 193/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0087 - val_loss: 0.0153\n",
      "Epoch 194/200\n",
      "760/760 [==============================] - 0s 111us/step - loss: 0.0089 - val_loss: 0.0157\n",
      "Epoch 195/200\n",
      "760/760 [==============================] - 0s 107us/step - loss: 0.0084 - val_loss: 0.0156\n",
      "Epoch 196/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0097 - val_loss: 0.0151\n",
      "Epoch 197/200\n",
      "760/760 [==============================] - 0s 118us/step - loss: 0.0107 - val_loss: 0.0151\n",
      "Epoch 198/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0087 - val_loss: 0.0147\n",
      "Epoch 199/200\n",
      "760/760 [==============================] - 0s 112us/step - loss: 0.0091 - val_loss: 0.0141\n",
      "Epoch 200/200\n",
      "760/760 [==============================] - 0s 110us/step - loss: 0.0090 - val_loss: 0.0142\n"
     ]
    }
   ],
   "source": [
    "hist=model.fit(x_train,y_train,epochs=200,batch_size=32,validation_data=(x_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'])\n",
    "plt.plot(hist.history['val_loss'])\n",
    "plt.ylim(0.0,0.15)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','val'],loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "look_ahead=250\n",
    "xhat=x_test[0,None]\n",
    "predictions=np.zeros((look_ahead,1))\n",
    "for i in range(look_ahead):\n",
    "    prediction=model.predict(xhat,batch_size=32)\n",
    "    predictions[i]=prediction\n",
    "    xhat=np.hstack([xhat[:,1:],prediction])\n",
    "    \n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(np.arange(look_ahead),predictions,'r',label='prediction')\n",
    "plt.plot(np.arange(look_ahead),y_test[:look_ahead],label='test function')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
